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        "%matplotlib inline"
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      "source": [
        "\n# Feature selection using SelectFromModel and LassoCV\n\n\nUse SelectFromModel meta-transformer along with Lasso to select the best\ncouple of features from the Boston dataset.\n\n"
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      "source": [
        "# Author: Manoj Kumar <mks542@nyu.edu>\n# License: BSD 3 clause\n\nprint(__doc__)\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import load_boston\nfrom sklearn.feature_selection import SelectFromModel\nfrom sklearn.linear_model import LassoCV\n\n# Load the boston dataset.\nboston = load_boston()\nX, y = boston['data'], boston['target']\n\n# We use the base estimator LassoCV since the L1 norm promotes sparsity of features.\nclf = LassoCV(cv=5)\n\n# Set a minimum threshold of 0.25\nsfm = SelectFromModel(clf, threshold=0.25)\nsfm.fit(X, y)\nn_features = sfm.transform(X).shape[1]\n\n# Reset the threshold till the number of features equals two.\n# Note that the attribute can be set directly instead of repeatedly\n# fitting the metatransformer.\nwhile n_features > 2:\n    sfm.threshold += 0.1\n    X_transform = sfm.transform(X)\n    n_features = X_transform.shape[1]\n\n# Plot the selected two features from X.\nplt.title(\n    \"Features selected from Boston using SelectFromModel with \"\n    \"threshold %0.3f.\" % sfm.threshold)\nfeature1 = X_transform[:, 0]\nfeature2 = X_transform[:, 1] \nplt.plot(feature1, feature2, 'r.')\nplt.xlabel(\"Feature number 1\")\nplt.ylabel(\"Feature number 2\")\nplt.ylim([np.min(feature2), np.max(feature2)])\nplt.show()"
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